This dissertation presents an empirical approach to identifying decisions for adapting cognitive radio parameters with no a priori knowledge of the environment. Cognitively inspired radios, attempt to combine observed metrics of system performance with artificial intelligence decision-making algorithms. Current architectures trend towards hybrid combinations of heuristics, such as genetic algorithms (GA) and experiential methods, such as case-based reasoning (CBR). A weakness in the GA is its reliance on limited mathematical models for estimating bit error rate, packet error rate, throughput, and signal-to-noise ratio. The CBR approach is similarly limited by its dependency on past experiences. Both methods have potential to suffer in environments not previously encountered. In contrast, the statistical methods identify performance estimation models based on exercising defined experimental designs. This represents an experiential decision-making process formed in the present rather than the past. There are three core contributions from this empirical framework: 1) it enables a new approach to decision making based on empirical estimation models of system performance, 2) it provides a systematic method for initializing cognitive engine configuration parameters, and 3) it facilitates deeper understanding of system behavior by quantifying parameter significance, and interaction effects. Ultimately, this understanding enables simplification of system models by identifying insignificant parameters. This dissertation defines an abstract framework that enables application of statistical approaches to cognitive radio systems regardless of its platform or application space. Specifically, it assesses factorial design of experiments and response surface methodology (RSM) to an over-the-air wireless radio link. Results are compared to a benchmark GA cognitive engine. The framework is then used for identifying software-defined radio initialization settings. Taguchi designs, a related statistical method, are implemented to identify initialization settings of a GA.